As shown in Table REF , and Table REF , we conduct experiments on the `machine_annot (M)' branch and the `ALL' branch of InterHand2.6M V1.0 dataset respectively. We compare the results with or without using our HDR framework. We notice that the recent state-of-the-art single-hand pose estimation (SHPE) method (MinimalHand [1]})
struggles with occlusions and appearance ambiguity in interacting hand scenarios (IH26M-IH). To tackle these challenges, we propose a novel Hand De-occlusion and Removal (HDR) framework to perform hand de-occlusion and distractor removal. In Table REF , we show that our approach significantly improves upon the SHPE baseline in interacting hand scenarios, e.g. from 40.98mm to 25.45mm (M, IH26M-IH). We find that adding AIH dataset for training will further improve the performance of SHPE, which validates the effect of AIH dataset. Experiments on the `ALL' branch have a similar phenomenon.
<TABLE><TABLE>